Multichannel parametric models for airborne phased array clutter

Abstract

Ground clutter is the dominant power contributor in most scenarios involving an airborne surveillance phased array radar. Further, ground clutter power is spread over a large region of the two-dimensional (Doppler and spatial) frequency plane, which complicates moving target detection. Conventional methods, such as joint- and factored-domain via sample matrix inversion, have been formulated to detect moving targets immersed in ground clutter and interference, but these methods have either high computational and data sample size requirements, or performance limitations. Alternatively, multichannel model-based methods have been formulated for moving target detection. Multichannel state variable models (SVMs) are applied to modeling ground clutter in airborne surveillance phased array radar scenarios. The SVMs are fitted to computer-generated data and to radar data collected in the Rome Laboratory Multi-Channel Airborne Radar Measurement (MCARM) program. The analyses include consideration of model order selection. The models are utilized to generate filters to whiten the clutter component. The modeling and whitening results presented demonstrate that ground clutter is modeled effectively using multichannel SVMs. Such models can be utilized for model-based target detection.